Graphmemory
Build and query embedded GraphRAG databases with DuckDB vector search
What You Can Do
Store entities and relationships in a persistent, embedded graph database. Run vector, full-text, and hybrid semantic searches across your graph structure. Extract entities and relations from unstructured text using DSPy, deduplicate nodes and edges intelligently, run graph algorithms like PageRank and centrality analysis, and visualize your knowledge graph interactively in a browser—all within a single Python package with zero server dependencies.
Features
semantic similarity search over embedded entities and relations
combine vector, full-text (BM25), and structured filters in one query
follow relationships across multiple levels to retrieve connected knowledge
automatically extract entities and relations from raw text with built-in deduplication
merge duplicate nodes by name similarity threshold (e.g., 0.9) without manual conflict resolution
PageRank, centrality measures, connected components via NetworkX integration
chainable API for intuitive graph queries without SQL
render your knowledge graph in the browser with zero dependencies
Example Output
Extract and store a knowledge graph:
Input: "Claude was created by Anthropic in 2022. Anthropic focuses on AI safety research."
Output nodes: [Entity(name="Claude", type="AI Model", created_by="Anthropic"), Entity(name="Anthropic", type="Company", founded=2022)]
Output edges: [Relation(src="Claude", relation="created_by", tgt="Anthropic")]
Semantic search and traversal:
Query: graph.search(query="AI safety company", top_k=5)
Result: [Anthropic (score: 0.94), Claude (score: 0.87), ...]
Multi-hop: graph.traverse(start="Claude", relation="created_by", depth=2)
Result: [Claude → created_by → Anthropic → founded_in → San Francisco]
What's Included
- SKILL.md: Complete API reference and decision table for insert, merge, search, and dedup operations
- Extraction template: DSPy-based prompt for entity and relation extraction from text
- Query builder examples: Copy-paste patterns for vector search, hybrid search, and multi-hop traversal
- Deduplication workflow: Step-by-step guide for fuzzy matching and node/edge merging
- Visualization snippet: HTML/JavaScript template to render your graph in an interactive browser view
Who It's For
- Data engineers and ML engineers building RAG systems that require graph traversal for retrieval
- Knowledge management teams extracting and organizing entities from research papers, documents, or domain-specific text
- Prototyping teams needing a lightweight knowledge graph without managing external databases
- AI researchers experimenting with DSPy-based information extraction pipelines
- Compliance and risk teams building interconnected entity relationship maps from unstructured data
Best For
- Knowledge graph construction and semantic search over entities and relationships
- Retrieval-augmented generation (RAG) where graph structure improves answer quality
- Entity and relation extraction from text with automatic deduplication
- Graph algorithms (PageRank, centrality analysis) on your stored data
- Interactive exploration and visualization of domain knowledge networks
You might also like
You can analyze healthcare interior design proposals across multiple regulatory frameworks simultaneously. Claude reviews spatial layouts, material selections, workflow patterns, and accessibility features against current FGI Guidelines, ADA accessibility standards, ICRA (Infection Control Risk Assessment) protocols, and state/federal healthcare facility codes. This systematic evaluation identifies compliance gaps, material risks, workflow inefficiencies, and accessibility failures before construction begins, helping you avoid costly revisions and regulatory delays.
by bradagi
You can rapidly audit building designs against applicable codes and regulations, identifying violations and required revisions before formal submission. This skill helps you cross-reference design elements against specific code sections, generate compliance reports with violation summaries, flag high-risk areas requiring specialist consultation, and prepare documentation for code official discussions—significantly reducing review cycles and design rework.
by bradagi
You can quickly validate commercial floor plans against building codes, identify circulation bottlenecks, and recommend spatial programming improvements without manual code review. This skill analyzes tenant layouts, egress patterns, ADA compliance risks, and functional relationships to accelerate schematic design decisions and support lease negotiations with data-driven feedback on space efficiency and code violations.
by bradagi
You can submit architectural design briefs—regardless of project type or complexity—and receive a comprehensive validation that identifies gaps in technical specifications, flags compliance and regulatory risks, surfaces conflicting requirements, and generates clarifying questions for stakeholders. This catches issues when they're cheapest to fix, preventing costly design iterations and scope creep during development phases.
by bradagi
Get expert guidance on designing efficient NoSQL schemas tailored to your access patterns and scaling requirements. You'll analyze query bottlenecks, receive specific optimization strategies with concrete examples, and understand trade-offs between consistency, availability, and performance across MongoDB, DynamoDB, and Cassandra.
You can develop complete interior design systems for hospitality properties that account for guest psychology, housekeeping efficiency, safety regulations, and revenue metrics simultaneously. This skill helps you create guest rooms, suites, common areas, and F&B spaces with strategic spatial planning and sensory design protocols that increase guest satisfaction scores, reduce operational costs, and optimize Revenue Per Available Room (RevPAR).
by bradagi
You can rapidly develop comprehensive space programming documents that quantify spatial requirements, define operational workflows, and establish design criteria before schematic design begins. This skill translates your hospitality venue's business objectives and guest experience vision into precise area schedules with net/gross calculations, adjacency matrices showing spatial relationships, and guest flow diagrams that guide all subsequent design decisions and ensure efficient space allocation.
by bradagi
You can input client briefs, building dimensions, departmental requirements, and adjacency needs to receive a comprehensive space program that quantifies square footage by function, identifies spatial conflicts, calculates efficiency metrics, and prioritizes areas by business criticality. This becomes your design contract—a documented baseline that prevents scope creep, justifies design decisions, and enables accurate cost estimation throughout the project lifecycle.
by bradagi